The high F-scores indicate that the meaning representations are often syntactically very similar, if not identical.

there is a considerable subset of meaning representations which are different from the English ones, indicating that there is at least a slight discrepancy in meaning for those translations.


A common trend across evaluations of WordNets extracted from lexical resources is that while the synsets themselves are reasonably precise, recall is often very low -that is, although extracted synsets are accurate when compared to reference WordNets such as PWN, not enough synsets are actually being extracted using automatic methods.

these results do not necessarily paint the full picture -there are few agreed principles or common guidelines for evaluating extracted synsets, and it is often difficult to decide what constitutes a correctly or incorrectly extracted synset.


More recently there have been several purely data-driven end-to-end approaches to sign recognition from continuous signing based on Recurrent Neural Net (RNN) architectures (Cui, Liu, and Zhang, 2017;Koller, Zargaran, and Ney, 2017).

the performance of these imagebased approaches is held back by limitations in the data- Figure 1: Framework Overview sets and the fact that they do not integrate linguistic knowledge and perform 3D analysis.


The handshape CNN returns top-1 accuracy of 70.1%; top-5 accuracy reaches 92.3%.

we use the entire set of handshape probabilities from the output of the neural net as features for sign recognition.


For example, learning performance was better for the Genbase dataset (LC: 1.252, LD: 0.046) as compared to the Medical dataset (LC: 1.245, LD: 0.028), where they had similar cardinalities but the Medical dataset was less dense.

performance was better for the Emotions dataset (LC: 1.869, LD: 0.311) as compared to the Yeast dataset (LC: 4.237, LD: 0.303), where they had similar density but cardinality of the Yeast dataset was higher.


Movie and TV subtitles are a highly valuable resource for the compilation of parallel corpora thanks to their availability in large numbers and across many languages.

the quality of the resulting sentence alignments is often lower than for other parallel corpora.


Web 2.0 has brought with it numerous user-produced data revealing one's thoughts, experiences, and knowledge, which are a great source for many tasks, such as information extraction, and knowledge base construction.

the colloquial nature of the texts poses new challenges for current natural language processing techniques, which are more adapt to the formal form of the language.


It's common belief that a "他"("him" or "he") is dropped from the sentence 2 .

if restored, the resulting sentence "我请他他吃饭。"(I invite him he eat meal, i.e., "I invite him to eat a meal.")


We investigate the impact of this in our experimental section (Section 5.6.).

ocpd and ocpd+ provide a posterior distribution on the run length, from which it is possible to automatically detect the number of change points.


This property helps the model to identify the similar entities.

it still suffers from several issues such as, they cannot handle out-ofvocabulary words, misspelled words and variations in noun or verb phrase.


This layer takes the output (sequence of probability vector) of the previous layer as input and produces the label sequence as the output.

the label sequence C 1∶n can be obtained for C i as follows: ).


The evaluation shows that our proposed system achieves higher performance compared to these systems.

it is to be noted that our reported results are on cross-validation and we have not been able to perform experiments on the test data as this is not publicly available.


Figure 2 shows an example for each case: Figure 2: Examples of relations for meta-language tokens in PoSTWTITA-UD.

if syntactically integrated within the sentence, these same elements are annotated taking into account their actual syntactic role.


In their corpus, 47.3% of points of interest are annotated invalid, meaning that their methodology to extract points of interest using Foursquare is not very effective.

to their work, we (a) present a corpus with few invalid locations (⇡ 6%), and (b) work with finer-grained temporal information (when somebody tweets, within 24 hours before and after he tweeted, and longer than 24 hours before and after he tweeted).


This is apparently based on an expectation that there should be a single way to encode any given kind of textual phenomena.

anyone with moderate awareness of the richness of modern day Digital Humanities will know that there is definitely no single way to approach the different kinds of data, information needs, visualisation requirements and the varying foci of interest of Digital Humanities' scholars.


by increasing the possibility of exceptions, or by simply enforcing the usage of different routines to be able to add and extract similar-level information to and from the text) that seem avoidable by allowing for the homogeneity of linguistic markup.

the TEI tagset can easily tolerate an addition of an encoding variant that provides a localized alternative to existing tagging solutions.


Current research in ATECC is mostly evaluated by using reference translations from a source other than the input corpus or by using only a limited set of manually evaluated term equivalents from the input corpus.

to accurately evaluate the entire output and be able to trace mistakes back to their source, a new type of GS is needed.


In the domain of heart failure, "ejection fraction" would be an example of a Specific Term.

no matter how well constructed the corpus, there may also be terms which are lexiconspecific, but not domain-specific.


The annotation scheme is helpful for deciding whether a linguistic unit is a term.

there is a second difficulty in term annotation, namely deciding the term boundaries.


For the English corpus on wind energy, there are even more 2WTs (49%) than SWTs (25%).

apart from the variations for SWTs and 2WTs, the numbers are rather consistent for all languages and domains.


We showed that, in general, terms are mostly SWTs or 2WTs and that very few terms are longer than 5 words.

specific Terms tend to be longer and less likely sWTs than Common Terms.


Each file is relatively small (around 150Mb) and is easy to download and work with locally during the development phase.

to work with the entire corpus we recommend using some kind of parallelism, e.g.


We do not distribute the index itself due to its huge size.

users can re-create the index using the open source software provided as a part of the JoBimText package 28 from the the CoNLL files.


UD schema defines that the basic units of annotation are syntactic words, though a renowned typologist failed to identify words consistently across languages (Haspelmath, 2011).

sUW is not suitable for syntactic dependency annotations.


The system used to manually disambiguate trees generated with Świgra (Woliński, 2010) includes a module to automatically re-annotate a parse forest generated with a changed grammar preserving the tree previously chosen by annotators.

in the form previously implemented, the system sought for a tree literally identical to the previously selected one.


Morphology was intensively studied by the NLP community, with the research primarily concentrated on inflectional morphology.

in recent years researchers noticed the potential of derivational morphology to improve the performance in many important areas of NLP, which caused the development of novel language resources which focus on word formation.


(2001) develop a system for automatic generation of morphological families 2 in order to improve their information retrieval systems.

the derivational rules are incorporated into the system by a human expert rather than automatically learned.


Learning to rank Learning to rank is a widely studied area of machine learning which was originally researched in the context of automatic ranking of web search results in the information retrieval community.

it proved to be useful in many other areas such as statistical machine translation, see (Watanabe, 2012).


The application of the SPADE algorithm with the minimal support set to the 1% of the lexicon size resulted in roughly 27 thousand frequent subsequences.

our filtering technique based on the phi coefficient limited the set of patterns to 13 441 regular expressions.


We evaluated our approach using 5-fold cross-validation and we obtained the accuracy of 82.33% without applying any threshold on the confidence of the ranker.

since we prefer precision to coverage, we have chosen a threshold which allowed us to obtain 98.8% of precision with the recall of 38.2%.


Furthermore, it is also the biggest free language resource about Polish derivations.

the coverage of the created resources still needs to be improved.


The number of documents also fluctuates over the years, mainly due to the biennial frequency of some conferences.

the total number of papers itself increases steadily reaching a total of more than 65,000 documents as of 2015.


We studied the evolution of the presence of the terms over the years, in order to check the changes in paradigm.

the fact that some conferences are annual, while others are biennial brings noise.


The statistical information provides a weighting of the extracted applicant terms.

the frequency of a term is not necessarily an appropriate selection criterion.


Q 1 > Q 1.1 ) that guide the occurrence of so called contrastive topics.

qUD trees can be systematically mapped onto discourse graphs from Segmented Discourse Representation Theory (SDRT; Asher and Lascarides, 2003).


It is, therefore, perhaps still too early to interpret the results, due to the overall complexity of the task and the lack of a reason-  Table 2: Kappa values for QUD-annotated spoken dialogue able baseline.

the results can provide a point of reference for future developments in this area.


In fact, given two arguments and a discourse connective many discourse parsers at the 2016 CoNLL Shared Task on Multilingual Shallow Discourse Parsing (SDP) (Xue et al., 2016) were around 78% accurate in recognizing the discourse relation on the SDP blind dataset.

in implicit relations no connective is used.


Inspired by recent advances in the use of attention, we used attention to detect alignment scoring for IDR as word-pair features have be shown to contribute to IDR (Pitler et al., 2009;Biran and McKeown, 2013).

unlike these methods we make no feature engineering.


(Rönnqvist et al., 2017) also uses an attention mechanism to recognize implicit discourse relations.

their approach differs from ours in two important ways: in (Rönnqvist et al., 2017), the two discourse arguments are concatenated to form a single input and the attention mechanism is applied over the entire input, which is fundamentally different to our sequence-tosequence approach.


We believed the model would be more robust if the classification layer had inputs from all decoded hidden states directly.

using only the final state vector resulted in higher classification score while using less parameters.


The hidden vector s t obtained after the last character is called the last feature vector, as it stores the information related to the character language model and the sentiment of the utterance.

it was shown that the average vector over all characters in the utterance works better for emotion detection (Lakomkin et al., 2017).


ANNIS' latest version, ANNIS3, provides a solution for RST trees by means of a visualization plugin (Krause and Zeldes, 2016).

the application is limited to only displaying previously annotated corpora.


For a fuller review of available corpora and the challenges of genre in conversation, see (Gilmartin et al., 2015a).

we are interested in the substance of longer casual conversation beyond these first encounters, and thus we have collected a number of multimodal recordings of conversations of multiparty casual speech to form a dataset for preliminary explorations.


For example, considering again sentence 208, the wordécartement, which is a morphological variant of the substituteécart, appears as a new valid substitute.

the annotators may find no answer for a given sentence because there is only one good substitute corresponding to a rare word (e.g.


As can be seen, for both measures the scores are scaled by the sum scores of all substitutes for the target word.

there are important differences from one target sentence to another, the number of substitutes with a positive score in the second gold standard varying from 5 to 56.


So the short answer is that this second annotation was not worth the effort, and we hope that this can be of use for future work on the development of such evaluation data.

the comparison of the two data sets gives us useful insights on the task itself, and helps us understand the gaps between the systems.


This decisionmaking conforms to the idiom principle (Sinclair, 1991) or formulaic language (Wray, 2001), which help the translators produce native-like selections and reduce the cognitive processing effort.

it seems that our BMWU alignments have less effect on fluency.


As can be seen, thousands of translation candidates were generated for each language pair.

not all of these word pairs are correct translation candidates, therefore we needed to extract the useful word pairs from the merged dictionary for each language pair.


And the clinicians evaluating the speech of patients are very well trained to the phonetic characteristics associated with the physiopathology of dysarthria.

a frequent criticism to perceptual evaluation is the subjectivity of the listeners (both naive and expert).


This capacity has been already highlighted in (Laaridh et al., 2015a) with 81% of phone-based anomalies annotated by an expert well detected by the system.

the low AG targetAnomaly rate of 13% observed on "false positives" reveals the limitations of the proposed approach and its somehow approximate judgment when facing more subtle anomalies.


This behavior is particularly evident on ALS patients on whom the jury annotated the most anomalies compared to other populations and where the AG targetAnomaly rate reaches 19.6% on the "false positives" category.

an opposite behavior is observed on CTRL speakers and PD patients for whom an overall good quality of the speech is usually observed and the computed AG targetAnomaly rate over the "ambiguous segments" reaches 15.2% and 42.7% respectively.


They compare their crowdsourced dialog data set to a smaller lab-based data set.

in terms of naturalness they do not compare the collected dialogs but rather subjective questionnaire ratings.


This can be due to the fact that the amount of training utterances is increased in this case.

this result should be viewed with caution.


As this guidance, VCOPA poses a rich set of challenges, many of which have been viewed as the holy grail of automatic image understanding and causal reasoning in general.

it includes several components that the KR and CV communities have made significant progress on during the past few decades.


Alternative 1 seems to happen before the premise, while Alternative 2 happens after the premise.

the question is asking for the effect, which means the latter alternative is the correct answer.


Building linguistic resources, such as corpora or dictionaries, can be very labor-intensive, requiring great amounts of work-hours and expert annotation.


As the table shows, there are variations between the Fscores: the first model (M1) has the lowest F-score, while the third model (M3) has the highest F-score, traditionally making M3 the clear choice.

our annotation budget is constrained, so the question we ask is: which of the machines will provide the maximum benefit in terms of the number of correctly identified VNICs, given our budget?


Much recent effort has been put into building Knowledge Bases (KBs), either manually curated (Freebase (Bollacker et al., 2008), Cyc (Lenat, 1995)) or automatically produced (YAGO (Suchanek et al., 2007), Knowledge Vault (Dong et al., 2014)), ranging from logically consistent linked-data in OWL (SUMO (Pease et al., 2002)) to little-structured sets of textual relations extracted from text (NELL (Mitchell et al., 2015)) with Open IE systems (Reverb, Ollie (Mausam et al., 2012), ClausIE (Del Corro and Gemulla, 2013), Stanford Open IE (Angeli et al., 2015), CSD-IE (Bast and Haussmann, 2013)).

large they may be, typical KBs are largely incomplete, and many relevant facts are missing (West et al., 2014).


The task of taxonomy extraction is closely related to tasks such as hypernym detection (Hearst, 1992) or ontology learning (Buitelaar et al., 2005), in which a structured representation of concepts should be learned.

the task of taxonomy extraction does not have the formal nature 1 http://www.acm.org/publications/ class-2012 that either of these tasks in that the terms only need to be loosely associated.


In fact, taxonomies are not intended to be strict hypernym graphs but may in fact contain other relations.

we use this as the basis for our experiments as this dataset is established and has been used by other systems.


Abbreviation prediction means associating the fully expanded forms with their abbreviations.

due to the deficiency in the abbreviation corpora, such a task is limited in current studies, especially considering general abbreviation prediction should also include those full form expressions that do not have general abbreviations, namely the negative full forms (NFFs).


LSTM shows competitive performance in this task.

neural networks usually need large data for training.


In other words, the relValue should be start with '+' symbol if the meaning of text is later than the reference date, and vice versa.

in a special case such as the last example in Table 1, there is no symbol to prefix because the meaning of text exactly pointed the specific date or time.


Frameworks that support general text mining, e.g., the General Architecture for Text Engineering (GATE) (Cunningham et al., 2011), provide "local interoperability" for tools available from within the framework, but there is no interoperability with tools or components available from outside the framework that the user might wish to use.

the LAPPS Grid provides interoperable access to tools in various UIMA systems as well as tools from GATE, which can be pipelined within the LAPPS Grid without the need for I/O format conversion.


The second experiment with the oracle LM indicates the current capabilities of the acoustic model.

the AM can be adapted using the available recordings to fit the actual acoustics conditions in the interviews.


82%)) to establish letter discriminability matrices for the Latin alphabet.

as many tables as were readily available from the supplied paper links have been extracted and it was ensured that they were labelled for 1. modality (visual, motoric, acoustical) 2. directionality (symmetric matrix?, ∆(< a >, < d >) 3. letter set (upper case, lower case, numbers, mixed case) 4. polarity (similarity or distance) some matrices or data reported in the papers were not used, since they either analysed irrelevant data (perception in pigeons (Blough, 1985), discrimination of the Braille alphabet, (Gilmore et al., 1979)), reported a poor predictive performance (Coffin, 1978), provided incomplete data (Uttal, 1969), featured very few observations (Banister, 1927) or were hardly extractable due to age or condition of the pdfs.


Naturally, one could choose grapheme-to-phoneme (g2p) and p2g based approaches.

since the aim of the present study is to analyse explicitly modally motivated errors, we alternatively do the following and leave g2p/p2g as an alternative for future research.


Indicating shape/size with deictics Deictic gestures have been extensively studied for positional information.

humans often encode more than positional information while "pointing".


This is what we do also observe in the WAW corpus when we compare the number of tokens of the Arabic translations (B2) compared with the transcript of the original speaker's speech (A1, in English).

when we compare the number of word tokens in the transcripts of the interpreters (B1) with the number of tokens in the translations of these transcripts (A2), we see a much smaller ratio.


These corpora are partially suitable to evaluate multimodal computational models for object-word learning.

they are not sufficient for crossmodal action learning.


Parsing at phrase-level is accurate except for nominal modifiers perhaps due to confusing usage of directional and temporal adverbial nouns and prepositions in Vietnamese.


The only tokens which are not defined are the output tags.

considering too many features overloads the model, so that CRF++ crashes without generating one.


AET was originally designed in a research project concerned with modeling the compositional interaction of attributive adjectives with nouns 4 and a project concerned with the interplay of events and adverbial modifiers 5 .

the structure of the AET database makes it easy to extend the tool to different research areas and languages or to add more corpora.


, syntactic chunks , dependency trees (Hwa et al., 2005), word senses (Bentivogli and Pianta, 2005), named entities (Mayhew et al., 2017) and semantic roles (Padó and Lapata, 2009;Akbik et al., 2015).

as the example in Figure 1 shows, annotation projection may not always produce fully annotated target language sentences.


Similarly to PKT, where -SBJ function tag denotes a subject node, KTB offers three morpheme tags for the same purpose: jcs, jcc, and jxt.

while jcs and jcc roughly correspond to nsubj and csubj, jxt suggests that the phrase is the topic of the phrase or clause, but offers nothing informative in distinguishing whether it is in fact a subject (which it frequently is) and, if so, whether it is a clausal or nominative subject.


We introduce the structure to the documents and annotate them on sentence and document level.

we exclude from this annotation process 100 collection documents, i.e., documents consisting of more than one article, since they have their own structure.


There have been some works on POS tagging in Amharic (Gamback B., 2012;Martha, Solomon, and Besacier, 2011;Binyam, 2010;Gambäck, Olsson, Argaw, and Asker, 2009;Sisay, 2005).

the work of Demeke and Getachew (2006), known as the Walta Information Center corpus (WIC), has received much attention among Amharic NLP researchers and has been used for different applications.


They are trying to give tag-sets for various syntactic constructions, (phrases, clauses and sentences) in addition to a syntactic word.

amharic is a less-resourced and morphologically-rich language where problems of OOV and ambiguities are major bottlenecks.


According to the guideline these elements are attached to nouns, verbs, pronouns, adjective and numerals.

some adverbs (for instance, ዛሬ /zare/ 'today') can attach a preposition and/or conjunction.


Specifically, nouns (verbal noun -VN), verbs (auxiliary -AUX, relative verb -VREL) and numerals (cardinal -NUMCR and ordinal -NUMOR) which have sub-categories with the respective specific tags.

when these sub-categories attach a preposition or a conjunction, their distinction from the other respective categories cannot be distinguished.


The nominal will be given the grammatical role of nsubj, obj, etc., while the clitics will be treated as a pronominal copy of the nominal and will get the role of expl.

when the nominal is dropped, the clitic will get the grammatical roles of nsubj or obj.


In most works of Amharic corpora, data are collected from electronic media, especially from the news media.

such sources are produced without proper text editing tools like a spell or grammar checker.


They demonstrated that a multilingual model can yield better results than monolingual models for different European languages.

their approach relied on the existence of a massive parallel corpus, as their experiment was based on Europarl.


Compared with the result of the Shared Task, it seems that our approach (lexicalized cross-lingual transfer parsing with resources from relevant languages) can be effective for parsing low-resource languages.

additional language features and the application of the ensemble mechanism also seem to be very important.


Tokenization, or morphological analysis, is a fundamental and important technology for processing a Japanese text, especially for industrial applications.

we often face many obstacles, such as the inconsistency of token unit in different resources, notation variations, discontinued maintenance of the resources, and various issues with the existing tokenizer implementations.


For many companies tokenization is a fundamental and important technology for text processing.

when increasing number of companies are demanding Japanese text processing recently, we are lacking freely available and useful resources for tokenization.


The user may select a resource for tokenization from publicly available choices.

iPADiC (Asahara and Matsumoto, 2003) is the most widely used resource for Japanese tokenization; it has not been updated since 15 years ago, therefore the dictionary lacks new words and the bug fixes have not been applied.


NAIST Japanese Dictionary 4 , a dictionary developed based on IPADIC, aimed to solve the license issues of IPADIC, as those issues make it difficult to use the resource for OSS purposes.

it is currently not widely used, as the dictionary lacks some essential vocabularies, and IPADIC license issues have been solved subsequently.


How to define the granularity of the token unit in Japanese tokenization has long been discussed.

the suitable unit differs for each application.


If the token is a noun, we define the unit to include its prefix and suffix.

if it is a verb we include up to the compound verb to be a middle unit.


We expect that a possibility to access useful information would increase by using these rules.

for example, '2 -(p -Tolyl)ethanol' has not been recorded on Nikkaji, '2 -(4 -Methylphenyl)ethanol' that is created by paraphrase rule has been recorded.


If we can identify the same chemical compounds with different notations by using paraphrasing rules, we can get information from different databases that register the same chemical compounds with different notations.

the new chemical compounds that have not been registered in databases cannot be found.


Wang (2013) uses Pedro Almodóvar's films La mala educación and Volver as the corpus to analyze how the subtitled Spanish discourse markers can be translated into Chinese, so as to make a guideline for translation education between the language pair.

none of these works use RST as its theoretical framework.


When a DM is removed, the distractors of the exercise are selected from those in the same group.

within each group, the DMs are grouped if it is almost impossible to distinguish between them.


Useful studies might look at how writing changes in longitudinal studies or as a function of particular training programs, thereby lending insight into quality of school books or teaching philosophies.

very little of this kind of validation is done on a larger scale or open to comparative research with open corpora.


It is not always obvious how this cleaning should be done, and we have experimented with several variants, for example in an earlier variant we removed the xxx markings.

this often led to clearly undesirable parses.


The SICI continuum has distributions of nouns and verbs that describe their individual difficulty differences.

no direct evidence has connected the concrete measure of word difficulty to abstract spaces because no index, other than the occurrence rate of the part of speech, has expressed word difficulty.


As a consequence, speakers of a free-stress language need to encode stress position in their mental representation of the words.

the position of stress in a fixed-stress language such as French is not variable, and thus not contrastive.


In the Present tense, the Habitual comes first, followed by the Negative: gatáagang-'ang-gang (eat-HAB-NEG-PRES) "never eats".

in the Reported Past, the order is reversed, with the Negative coming first and the Habitual coming second: gatáa-'ang-gaang-aa-n (eat-NEG-HAB-REP-PAST) "never used to eat".


2011;Hulden 2009), implement a minimization procedure on the finite-state model, so that recurring realizations of string-final character sequences and associated morphological features are systematically identified and merged, resulting, in the end, in a relatively compact model.

if some aspect of the chunked morpheme sequences needs to be changed, with the chunking strategy these have to be implemented in multiple locations.


It is a Germanic language whose nearest relative is Icelandic; the two are not normally mutually intelligible in speech to unpractised listeners, and Faroese is considerably more different (certainly not mutually intelligible) from its next closest relatives, the larger and better-resourced Mainland Scandinavian languages Danish, Norwegian, and Swedish.

many native speakers of Faroese do speak Danish as a second language, in current times often used for university education or employment in Denmark (while Faroese is the language of home, daily life, school, etc.


In the first place, automated OCR is unsatisfactory; then, we find also that building an entirely automated post-processing system, although it improves the text, still leaves far too much error.

incorporating a moderate investment of annotator time during post-processing leaves an acceptable token error rate below 1.5% in this low-resource setting.


Digitised resources could be distributed as scanned images rather than machine readable text, and communities may still access the heritage and culture represented in the resources.

text facilitates better distribution due to reduced file size, and large corpora are not easily navigable for any purpose without searchable text.


English (Nakov et al., 2013), German (Cieliebak et al., 2017), French (Bosco et al., 2016), or Italian (Barbieri et al., 2016).

we are not aware of any sentiment corpora for Swiss German.


Extension of the Resource As described in Section 5.1, J-MeDic contains 51,784 new written forms; 49.0% of those were newly incorporated.

44.7% of the disease names that are covered by J-MeDic were newly incorporated written forms.


It has meanings, example sentences, syntactic patterns and actual sentences from the corpus that they possess.

it has no relationship information with another words, such as synonymous words and phrases.


CVL would be bundling cluster1 and cluster2 in our list by the same core meaning.

in our result, synonym expressions in cluster 1 and those in cluster2 are clearly divided because they are used in a different context by figurative meaning.


As stated in the guidelines, we want to capture the senti-ment of the expression's common usage.

we are also interested in the ambiguity that is reflected in the answer distribution.


These are only two examples and there are others with ambiguity that is more debatable.

we think this highlights the advantage of having positive, negative, and neutral annotation so that we may have more insights into the ambiguity, e.g., mixed sentiment with equal percentages for positive and negative, or weak sentiment with a low percentage of positive (resp.


The best model for relatedness scores is the skip gram lemma based model with negative sampling, while for the similarity, the best model is the lemma based CBOW model with negative sampling and the size of window equal to 1.

both models are also nearly the best in the other task.


Many online news outlets apply the same journalistic principles that have been in use for newspapers for decades, especially factchecking.

there are also websites whose primary mission is not to provide high-quality journalistic reporting but, basically, any type of content as long as it produces as many clicks as possible, generating revenue through online advertisements.


Similarly, custom solutions exist that convert the results of a specific NLP pipeline into a pre-defined knowledge base.

so far there existed no generic solution for populating a knowledge base with the results from a text mining pipeline.


Generally, exposing implementation details in URIs is discouraged (Wood et al., 2014).

re-running NLP pipelines (and therefore re-generating triples) is an extremely common occurrence in language engineering -for example, after improving a component, a machine learning model, or experimenting with different parameters.


NLP2RDF 15 is another approach for converting NLP tool output to RDF (Hellmann et al., 2013).

the goals of NLP2RDF are largely orthogonal to the ones from LOD-eXporter: NLP2RDF focuses on the representation of NLP data (such as, words, sentences, strings), with the primary goal of providing an exchange format between different NLP tools.


datasets for geographical and language data.

we did not reuse these because it requires a large amount of manual mapping effort to retrieve the ca.


Further, the bibliographic references that correspond to a concept can be selected as another view within the concept data box while the term data above stays visible.

a detailed search interface for the bibliographic resources has not been implemented.


19 Ur III administrative data are comparably easy in terms of morphosyntax, since morphology is often not expressed in writing (though the information may be inferred from the context).

this also means that morphology is somewhat uninformative, so that effective querying and searching of these data requires structural analysis.


Their model formalizes three different collections in a coherent model: the CDLI catalogue, the ObjMythArcheo database, 39 a corpus of archaeological objects related to mythological iconography, and Bib- 35 From the CRM documentation, the superproperty crm:P128 carries would suit too, see http://www.

cidoc-crm.org/html/5.0.4/cidoc-crm.html# P128 hence, it must be a physical thing, in our case a E84 Information Carrier if no ?composite is found, then a separate crm:E34 Inscription must be created.


For the time being, KorAP does not offer facilities for querying the speech data, so we continue to use our own interface.

as it will become available, all CoRoLa data (text and speech) will be searchable in a single unified environment (KorAP).


Some results will be transferred to the industry for further development and industrialization and their distribution will be restricted.

most outcomes of the mentioned projects will be freely accessible to the interested public.


A general rule restricts relative markers to nonabsolutive non-finite forms.

a corpus search reveals that the situation regarding absolutives is more complicated: In Abkhaz, absolutives are used in serial verb constructions, where the absolutive and the verb following it are part of the same event.


Note that, some idioms have multiple idiomatic equivalents in other languages while others have none.

some idioms have definitions with almost equivalent syntactic structures while the semantics of the definitions are very different.


As factuality tags, we use factuality values proposed in FactBank (Sauri, 2008).


To be specific, the noun visit takes two semantic arguments, the visitor and the visited entity, as in 'the visit of the Queen to the Prime Minister'.

when used in to pay a visit, the semantic argument, visitor, is realized as the subject of to pay (The Queen paid a visit to the Prime Minister), and cannot be realized at the same time within the NP headed by visit (*The Queen paid a visit of the Lady to the Prime Minister).Paul transmitted the advice to his sister + Peter's advice →Paul transmitted Peter's advice to his sister.


(2014) constructs an MWE-annotated corpus based on English Web Treebank (Bies et al., 2012).

the number of VMWE occurrences (1,444) and types (1,155) in their corpus is relatively small-scale.


Konbitzul was originally created as an NLP-applicable resource.

with its user-friendly interface, it can simply be used as a phraseological dictionary as well.


wind turbine) are very common in English specialized texts (Nakov, 2013).

all too frequently they show similar external forms but encode different semantic relations because of noun packing.


Once again, this figure was 100% when the PROCESS category was further refined to LOSS PROCESS and ADDITION PROCESS (see Table 13).

there were some exceptions, such as volcanic eruption, which did not activate the relation has_patient, since volcano cannot be considered the patient of eruption but its agent.


As can be observed in Table 8, the values for some of the features, such as the coordination of passivization ratios are rather similar in both corpora.

each corpus also shows its own morpho-syntactic profile.


Figure 6 describes the deep convolutional encoder architecture first used by a Facebook machine translation system (Gehring et al., 2017).

to the RNN, convolutions do not provide explicit way to encode the position of a word in the input sequence.


As a result, the words associated with a literal target will have larger projection onto a target σvn.

the projections of words associated with an idiomatic target VNC onto σvn should have a smaller value.


The resulting model in (op Vollenbroek et al., 2016) was the overall winner of the PAN-2016 crossgenre author profiling task (Rosso et al., 2016), obtaining 0.53 joint accuracy (i.e., age and gender prediction put together) when combining results from English, Spanish and Dutch texts.

the authors pointed out that their present (cross-genre) results were considerably lower than those obtained in previous (single-genre) author profiling tasks at the PAN-CLEF series.


(Hasanaj, 2012) also attempts an evaluation of the two tagsets using a maximum entropy tagger and a perceptron tagger.

his gold standard corpora consist only of 263 tokens for the basic tagset and 641 tokens for the large tagset.


Using the full tagset, the best tagger achieves 91.00% accuracy -a good six points less than the state of the art for languages like English, French or German.

we have to take into consideration that our corpus is orders of magnitude smaller than for example the Wall Street Journal part of the Penn Treebank and that it is at the  same time much more heterogeneous.


Over the past 25 years, the Linguistic Data Consortium (LDC) has been the main provider of annotated data and annotation standards.

there are two main concerns with the LDC data (Marcus et al., 1995;Maamouri et al., 2003;Lander, 2005) that poses somes questions about its reliability and relevance to contemporary language.


UniMorph resources are rarely original resources, but rather extracted from existing material, 6 such as Wiktionary (Kirov et al., 2016, first-generation UniMorph inventories) and other dictionaries, bootstrapped from morpheme inventories or corpora (as described here), or generated by rulebased morphologies.

this conversion-based approach means that the segmentation and annotation principles of the underlying resource tend to be preserved.


mWda-lli mountain-ABL 'from the mountain'.

it can also attach to three other cases.


If that would be true, multiple cases could always be resolved unambiguously, in that any non-locative case is by definition the inherent case.

the examples given above involve several non-locative cases or even a reversal of this pattern, so that an explicit mechanism to express multiple case marking is required.


While the text data from micro-blog platforms like Twitters are very convenient and easy to collect, the fact that they are limited in the number of characters (140 for a tweet) differs themselves from daily conversation text and therefore, have limited use in a real-life settings.

multiclass scheme has its own limitation.


If one emotion exists in one word of the input then the corresponding tendency feature will be set to 1 and 0 otherwise.

solely relying on text would cause problem for emotion detection system.


As we can observe, our simple system are performing worse than human annotators by a considerable margin.

in comparison with other corpora 's Interannotators agreement F1-score such as Twitter Emotion Corpus (the score is 43.7), we see the potential of the corpus: It is reliable and the performance of emotion analysis system on it is great.


For example, in "rienà voir avec le seigneur des anneaux, carrément passionnant" [(this film has) nothing to do with the Lord of the rings, (that is) downright fascinating], (1) the phrase "downright fascinating" indicates a very positive opinion, but it is applied to another movie: "the Lord of the rings".

the full phrase indicates a negative feeling about the movie.


The use of lexicons has sometimes been straightforward, where the mere presence of a sentiment word determines a given polarity.

negation and intensification can alter the valence or polar-ity of that word.


After many years of research in sentiment analysis, the types of documents that have been dealt with in the field are varied.

two types stand out among the rest: user reviews of products or services, and microblogging short texts, particularly Twitter data.


Such unstructured textual information contains valuable information for marketing analysis.

customer loyalty has long been a topic of high interest in both academia and industry (Griffin, 2002;Asuncion et al., 2004;Scriosteanu and Popescu, 2010), and is critical for business profitability.


Tweets are usually multilingual in nature and of varying quality.

machine translation (MT) of twitter data is a challenging task especially due to the following two reasons: (i) tweets are informal in nature (i.e., violates linguistic norms), and (ii) parallel resource for twitter data is scarcely available on the Internet.


Social media has evolved from being cyber world geek buzz to a massive platform for businesses, entrepreneurs, professionals and organizations that seek greater recognition and identification at a very economical price.

business information sharing is not the only aspect of web services, e.g.


Recently, Twitter has gained massive popularity and the number of Twitter users has increased significantly during the last few years.

twitter users are often encouraged to write informal texts due to the 140-character limitation.


It can be observed that when only the Twitter data is used, better BLEU, METEOR and TER scores are obtained without using the sentiment classification ("Sent_Clas") approach ('Twitter (Baseline)').

the sentiment preservation score ("Sent_Pres") is higher when using the sentiment classification (72.66%) method whereas switching it off causes the score to be reduced to 66.66%.


Initially we restricted the sentiment classes to only negative, neutral and positive.

in future, it can easily be extended with some other sentiment classes such as strong negative, strong positive etc.


Given a sarcastic text, sarcasm target identification is the task of extracting the subset of words that indicate the target of ridicule.

in some cases, the target of ridicule may not be present among the words.


In this sentence, the words 'chefs', 'cooktop' and 'phone' are candidate phrases.

only the 'phone' is being ridiculed in this sentence.


In this work, we have focused on annotating a corpus of general student feedback, which contains more noise and complex data compared to feedback collected through reflective prompts.

unlike traditional reflective prompts, general feedback contains more useful information.


If certain statistical properties are ensured about the appearance of elements in the tuples, then the number of times an element is chosen as most positive minus the number of times it is chosen as most negative can be used as a sentiment score.

we experienced that if the BWS annotation is performed by directly sampling from the lexicon (or from a general corpus, for that matter), most 4-tuples would not contain any items with a clear non-neutral polarity, let alone one most positive and one most negative item.


On the one hand, during the training phase (phone-level alignment, creation of acoustic models, henceforth AMs), the phonetic transcription provides information about where to expect recurring patterns in the acoustic signal, which will be abstracted away and generalized in the acoustic models for the individual (tri)phones.

during decoding, it mediates between (to borrow a neurolinguistic analogy) the top-down (predictive) processes based on the linguistic knowledge (the language models, henceforth LMs), and the bottom-up (recognitive) processes based on spotting acoustic patterns in the incoming sound data.


On one hand, if a user's question does not exist in the forum, a new post is created so that other users can contribute and provide answers or comments.

if similar or related questions already exist in the forum, the system should be able to detect them and redirect the user towards the corresponding threads.


If this hypothesis can naturally be discussed and criticized, we did an extensive manual verification and we found a strong correlation between users score votes and correct answers.

the voting score can be adjusted to a certain threshold that guarantees reliable answers 5 .


This might be obvious while answers does not contain only lexical similarities with there corresponding questions.

using a mapping matrix to learn embedding regularities has shown interesting results (MappSent approach).


might need only take the form of explaining that one of bark's primary functions is to provide protection for the tree.

for a domain novice, such as an elementary science student, this explanation Bark is a protective covering around the trunk and branches of a tree.


If an inference method requires 2 shared rows, the corpus requirements would increase to approximately 2,500 questions to answer 80% of questions.

if an inference method requires 3 or more rows, this likely would not be possible without a corpus of at least 20,000 questions and explanations -a substantial undertaking.


Since the FrameNet frames are designed for general purpose, the annotated frame are not necessarily useful for the database search task.

we annotated the information that is relevant for constructing database queries regardless whether it corresponds to the database field or not.


supervised data, to achieve optimal performance.

because IQA is a challenging task that handles complex input and output information, the cost of naive manual annotation can be prohibitively expensive.


Roughly speaking, the first three approaches aim to obtain better representations or decision boundaries using unlabeled data.

it is difficult to directly apply these methods to multi-input tasks because multi-modal data often has complex structures, making it unclear how to improve representations or decision boundaries efficiently.


From a theoretical viewpoint, it is not very surprising that our method outperforms the baselines since our method uses additional information (captions) to generate questions.

considering that we can now easily obtain image-caption pairs from the web, we believe that our method is a practically promising approach to improve an IQA model with less human effort.


However, such existing training resources for these task mostly support only English.

we study semi-automated creation of the Korean Question Answering Dataset (K-QuAD), by using automatically translated SQuAD and a QA system bootstrapped on a small QA pair set.


As the advent of deep learning techniques turns algorithms data hungry, building resources of a significant size is a critical task, question answering (QA) systems, as demonstrated not only by SQuAD, but also by ImageNet for object recognition (Deng et al., ).

existing datasets for these tasks are mostly built on English.


Most of the existing works focus on retrieving answers in the same language in which the questions are posed.

with the rapid growth of multilingual contents on the web, it is necessary to build an automated system that retrieves information from the documents written in multiple languages.


The direct comparison of our dataset with the Cross-Language Evaluation Forum (CLEF) datasets (Pamela et al., 2010) is not possible because we have created question answers pair in both language (MQA) in contrast the CLEF dataset have the question and answer pair in the different languages.

we have shown the comparison in various terms as shown in Table 6.


(2016) give an important contribution to the understanding of HS in relation to other social phenomena: by collecting geo-tagged data from Twitter, the authors create a Hate Map that locates the breeding grounds of five different types of hateful content (Homophobic, Racist, Sexist, Anti-semitic and Against disability).

contrary to the approach we have followed in this work, the keywords used for filtering the data consisted of swear words frequently used against the five HS targets.


Needless to say that this kind of approach has several drawbacks, being a strong disagreement one of those.

in this work we rather look at the latter point as a signal (in Aroyo and Welty's (2015) words) of the inherent complexity of the task, given in particular by the potential ambiguity of the data at hand, as well as of the possibile solving strategies that can be put forward.


Such findings are quite consistent with our interpretation and definition of implicit incitement as typically based on, and promoting, prejudices, discrimination and hatred against a given target group (see Section 4.1.).

stereotype largely co-occurs also with higher degrees, which suggests us that it might be considered a fundamental factor in the definition of HS.


The choice of such a rich and fine-grained scheme is not flawless, nor without drawbacks, all highlighted and discussed in this paper.

namely due to its greater complexity, the corpus lends itself to more detailed and systematic analyses of the possible linguistic patterns associated not only with HS itself, but also to all the other categories included in our scheme.


Using textual features extracted from users' communications, profiles or lists seems a natural way for modeling their interests.

this information source has several drawbacks when applied to large data streams, such as the set of Twitter users.


Popular platforms in this area are Flixter, themoviedb.org and iCheckMovies.

many of these platforms use the IMDb database, owned by Amazon, which handles information about movies, actors, directors, TV shows, and video games.


Further note, as we explained in Section 3., that the process of extracting interests from messages is almost free of errors (96% precision), while inducing interests from topical friends and subsequent mapping to Wikipedia has an estimated 10% error rate.

as mentioned in Section 1., semantic techniques can be applied to reliably identify the main categories of interest for each user, an enhancement that we leave to future work.


Large datasets are particularly of use in this context due to the complex nature of differential responses to gender.

previous computational work on language and gender has focused mainly on language about or portraying persons of a particular gender (Wagner et al., 2015;Flekova et al., 2016;Agarwal et al., 2015).


(2014) note that commenters are more emotional when the presenter is a woman.

existing sources of this data such as 3 https://www.ted.com/talks https://www.idiap.ch/dataset/ted are not labeled for presenter gender.


Interestingly, sentiment in responses to women was higher across the board; whether this represents "benevolent sexism" or genuine positive sentiment is an interesting and complex topic for future research.

the contexts represented by each dataset acted very differently.


A detailed review is provided in (Fernández Gallardo, 2016).

the newly acquired NSC corpus presents: • Clean speech recordings made in the acousticallyisolated Nautilus room (which gives name to this database) with the high-quality AKG C 414B-XLS microphone.


These findings indicate the plausibility to classify perceived speaker traits based on speech features related to their voice descriptions.

the voice descriptions of more speakers need to be analyzed in order to better determine the statistical effects between the SC and VD dimensions.


The formant tracker PRAAT showed systematic errors with male speakers, while SNACK and ASSP performed better on male than on female speakers.

when comparing the average RMSE error value for female speakers in PRAAT (194Hz) with male speakers in SNACK/ASSP (234/177Hz) we can see that the performance was in the same range (underlined values in Table 2).


Moreover, the training environment lacks the noise that is massively present in the real-world VHF radio communication; this might lead to a drastic decrease of the unprepared controller's ability to understand the communication once he is put into service.

the human pseudopilot usually handles several virtual airplanes.


The difference between predicted and true chunk boundary is half the duration of the pause, which may be several seconds.

the error is less severe than its magnitude suggests, since all words except one end up in the correct chunk.


For Latvian and Lithuanian languages the situation is better, for example, there are a 100h Lithuanian speech corpus created for the LIEPA project 1 , an 84h corpus from BMMG (Alumäe and Tilk, 2016) and a 100h Latvian speech corpus (Pinnis et al., 2014).

even this is not much, as neural network acoustic models can make use of many thousands of hours of data and achieve significant improvement in recognition accuracy.


This content can be automatically collected and processed for the purpose of training statistical models.

in many cases, this content is not structured or organised in an accurate and machine-readable form.


This mapping then can be used to help ASR to recognise this part correctly and produce a better alignment.

before that, there is an optional intermediate second alignment step that is needed to improve the alignment and get more anchor points.


This means that our existing ASR already recognises such segments correctly.

after adding these segments to the training, we see an improvement in WER.


Making an ASR system work well for Indian English would involve collecting data for all representative accents in Indian English and then adapting Acoustic Models for each of those accents.

given the number of languages that exist in India and the lack of a prior work in literature about how many Indian English accents exist, it is difficult to come up with a set of canonical accents that could sufficiently capture the variations observed in Indian English.


Recently, it has been shown that using native language (L1) data could help adapt Acoustic Models to an accent influenced by that L1 (Aditya Siddhant, 2017).

in the case of IE, we face the issue of not knowing which native language(s) should be chosen for adaptation.


Retrieved results are displayed inside the rings, and users can filter and browse them in real time.

a large-scale database system called "SHACHI" 1 was developed to collect metadata, such as tag sets, formats, and recorded contents, from language resources worldwide (Tohyama, 2008).


Results revealed that the best performance is obtained when English is used for both training and testing.

in the cases which the users are only capable of speaking their mother-tongue, it is best to use that language for both training and testing.


Relying on form-filling strategies, with predefined dialog states sequences, falls short of providing a flexible and adaptable system that supports natural conversation, where the interaction is not constrained by a fixed plot with one precise goal.

treating utterances or messages independently from each other fails to recognise the interconnected dialog structure and phenomena that are crucial to correctly understand and generate dialog interactions.


In recent years, the importance of dialogue understanding systems has been increasing.

it is difficult for computers to deeply understand our daily conversations because we frequently use emotional expressions in conversations.


It is desirable to construct a corpus through gathering naturally occurring dialogues from somewhere and annotating them with feature change information.

to do this ideal construction procedure, there are problems described below at least: (1) it is difficult to get large scale dialogues, (2) there is no guarantee that utterances gathered from natural dialogues always have simple structures.


the recorded data together with the survey materials (personality tests and experience assessment questionnaires) will be made open for reuse and repurposing.

since the core information is located in the audio and video signals, complete data anonymization was not an option.


our objective is not to learn a model (in a machine learning point of view) but to extract automatically and manually information to model the virtual patient's behavior (Porhet et al., 2017) that, then, will be validated through perceptive studies.


Furthermore, different approaches, which involve the characteristics of sounds and prosody based on speaking styles and the expressions of emotional speech classification in anime films, have also been proposed (Hara and Itou, 2010).

these studies only focused on non-verbal information for recognizing/classifying types of emotions without addressing verbal content.


Among the deep learning frameworks, TensorFlow is arguably the most widely used, as the Github statistics in table 1. demonstrate.

as far as we know (see section 2. for a more detailed description of the existing tools), there does not exist a toolkit that allows one to quickly design, train and test their own 'baseline' LMs with Tensor-Flow.


Characterbased models can be generalized in a sense that they do not require a pre-trained morphological analyzer, and they enable to calculate vector representations even for out-ofvocabulary words.

vector representations of characters present the same problem as those of words, due to a trade-off between vocabulary size and token frequency.


All words for people, animals, and trees are animate, while most other forms are inanimate.

there are many words such as asikan 'sock' or kôna 'snow' 1 that are semantically inanimate but behave as animate nouns in Plains Cree.


(1) Algonquian person hierarchy (adapted from Wolvengrey, 2011, p. 57) 2 > 1 >> 3 > 3′ >> 0 Both nouns and demonstrative pronouns may stand alone as the arguments of verbs, though nouns and demonstrative pronouns may also co-occur.

due to extensive verbal morphology (discussed below), it is more common that no overt arguments are present.


Additionally, Vandall and Douquette (1987) speak mostly about history and relate others' stories, so there are many third person verbs, which can potentially occur with multiple overt arguments.


FastText performance shows the lowest variance, i.e., it robustly yields good results across many different hyperparameter settings.

bPEmb and characterbased models show higher variance, i.e., they require more careful hyper-parameter tuning to achieve good results.


Personality-dependent lexical choice does seem to make more accurate decisions for most input properties, including even those with a relatively small number of instances.

a post hoc analysis suggested that the use of personality information is particularly helpful in the lexicalisation of affective information (e.g., properties conveying attributes such as smile, emotion etc.


Similarly to how the syntactic structure of idioms is described in the syntactic module, the LF rules of the semantic module are templates with placeholders for deep lexical units and names of syntactic relations.

these templates are a lot more intricate and diverse.


This is desirable of course, to avoid putting, say, a verb where a noun is needed.

the downside is that there is no guarantee that such a lexicalization exists for a given node that satisfies the constraints, so we might end up with a dead-end structure.


(2016) show that it has better potential than the EW-SEW dataset for the TS task.

up until recently, the Newsela corpus was only provided with alignments at the document level.


The second is not surprising, given that the Wikipedia dataset (Hwang et al., 2015) does not allow for one-to-many sentence alignments and therefore does not contain sentence splitting examples.

when trained on a dataset that contains examples of sentence splittings (the Newsela dataset), our NTS models were able to learn this simplification operation and successfully apply it on a test set from either the same domain (examples 2a-2c, Table 8) or from another domain (examples 5a-5b, Table 8).


That is probably the reason for the much lower number of NE errors by the NTS systems trained and tested on the Wikipedia dataset.

the large number of NE errors made by the NTS models trained and tested on the Newsela dataset does not seem to have greatly influenced the overall performance of the NTS systems (see Table 6).


We acknowledge that more work is needed to make sequence-to-sequence models flexible enough for handling out-of-vocabulary words, especially in a cross-domain text simplification.

neural TS systems were still able to produce grammatical output and correctly model sentence splittings and sentence shortenings even across different text genres.


For entities, it is straightforward what this referential world is.

compared to entities, events are less tangible (Guarino, 1999; for various reasons: 1. we use a small vocabulary to name events, which results in large referential ambiguity 2. events are more open to interpretation and framing, which leads to more variation in making reference 3. events are less persistent in time than entities 4. each event has many idiosyncratic properties, e.g.


Our annotation is more restricted to the specific events of this database only.

it can be extended to other domains by defining a different event schema.


For example, the most common expressions for the event type Death can be detected by correctly identifying the WordNet synsets kill.v.01 (cause to die; put to death, usually intentionally or knowingly) and killing.n.02 (the act of terminating a life).

this is not the case for all expressions in the GVC.


Moreover, RDF has demonstrated a promising ability to support the creation of NLG benchmarks (Gardent et al., 2017).

english is the only language which has been widely targeted.


Hence, the determiner to be used in front of this predicate needs to be "o".

to dbo:birthDate ("data de nascimento"), the word "data" is feminine, thus the determiner must be "a".


To this end, we handle the ambiguity of possessive pronouns by interspersing the alternative forms, e.g., dele (eng:his) or dela (eng: her)" which agrees with the subject.

it is used just in case more than one subject exists in the same description.


Consider the following triple, (:Albert Einstein dbo:nationality :Áustria), its objectÁustria is a demonym and should be lexicalized as an adjective.

it is lexicalized as a noun because the part-of-speech recognized by RDF2PT considers only the label Austria, which is a noun, and does not consider the predicate nationality, which is an important part, thus decreasing the quality of the generated texts.


We looked at a large set of musical pieces, trying to map notes, or chords, to words, and find a good mapping, so that any musician would have to invest little effort in adapting to the mapping, starting from the music (s)he already knows.

defining a good mapping is not an easy task.


In Figure 1 we show parts of the current MIMEdh web pages for two cornets and a bassoonthe information displayed comes directly from the database and is not very engaging for a museum visitor.

figure 2 shows a mock-up of a potential visitor experience of a virtual web musem visit using texts generated by Methodius using the techniques we will describe below.


The extraction process also has to be generic, because of the large number of lexica it would be too cumbersome to craft a targeted extraction algorithm for each webpage.

the pages do not offer structural patterns which could allow for a reliable boilerplate and metadata extraction (Barbaresi, 2016).


Among others, the construction of the Penn Treebank (Marcus et al., 1993) made possible the existence of many statistical parsers trained on syntactic treebanks which perform at an accuracy of about 90% (Charniak and Johnson, 2005).

computational semantics is lagging behind in this respect, as most of the semantic annotated resources for English are rather scattered or simply nonexistent for the large majority of languages.


Furthermore, AMR tries to abstract away from both morphological and syntactic idiosyncrasies that account for several crosslingual differences.

there is still a problem with this approach, as it is strongly biased (by design) to annotate English sentences.


o Morphologically related terms can appear in a family labeled "Word family" colored in purple.


These distinctions would be much more difficult to account for in textual format.

it should not replace the textual resource altogether since other forms of information are better represented in textual format (definitions, contexts, etc.).


It avoids modeling very long sequences, as the character-based models do, by preserving trivial compositionality in consecutive alphabetical letters and digits.

the separation between letters, digits, and special tokens explicitly represented most of the idiomatic syntax of Bash we observed in the data: the sub-token based models effectively learn basic string manipulations (addition, deletion and replacement of substrings) and the semantics of Bash reserved tokens such as $, ", * , etc.


• BLSTM (Bidirectional LSTMs): Bidirectional LSTMs were first proposed by (Graves and Schmidhuber, 2005).

the underlying concept of bidirectional recurrent neural networks was proposed by (Schuster and Paliwal, 1997).


This could be a lack of generalization during training due to the reduced size of training data using the 128 h subset.

since both the LSTM and Chain B model perform better than the BLSTM, we neglect the BLSTM in the following experiments.


The longer the distance between two data points, the less related the data points are.

this is usually not the case for real multimedia signals including text, sound and music.


Experiments with different content of the acoustic training data indicate that careful selection of phonotactical data could be advantageous when developing a speech corpus of limited size.

due to the extra effort needed to collect such data and the possible loss in recording quality due to reading mistakes, a clear positive impact of special phonotactical data should be evident.


Speaker modeling is in fact important to dialog systems, and has been studied in traditional dialog research.

existing methods are usually based on hand-crafted statistics and ad hoc to a certain application (Lin and Walker, 2011).


The LSTM-RNN attends to speaker s i to obtain speaker vector s i while it is encoding current utterances.

such attention mechanisms bring little improvements (if any).


A plausible explanation is that training a hybrid model as a whole leads to optimization difficulty in our scenario; that simply interpolating well-trained models is efficient yet effective.

the hyperparameter g is sensitive and only yields high performance in the range (0.6, 0.9).


The training of an automatic speech recognition system is usually straight forward given a large annotated speech corpus for acoustic modeling, a phonetic lexicon, and a text corpus for the training of a language model.

in some use cases these resources are not available.


BLSTM-CTC AMs play an important role in end-to-end automatic speech recognition systems.

there is a lack of research in speaker adaptation algorithms for these models.


Regardless of which text prompts are selected, or how they are recorded, the outcome of this process is a set of text and audio files with corresponding contents.

before these files can be used to build a synthetic voice for MaryTTS, they have to be phonetically annotated.


Although such synthesis can sound rather buzzy and unnatural, these HMM-based voices offer higher flexibility and more consistent quality than unit-selection synthesis, as well as a much smaller memory footprint.

some drawbacks are (a) that building HMM-based voices for MaryTTS has a high technical overhead, and (b) that the Java port has become quite outdated, while HTS development has seen significant progress.


1 A fully unsupervised method of calculating the speech rate estimation based on syllable nuclei detection was presented by de Jong and Wempe 2009as a Praat (Boersma and van Heuven, 2001) script, though we have yet to test this method on the data of the ILMT-s2s corpus to verify whether similar reliability can be obtained.

analysis of the syllables per second may not provide any better results since an analysis of the Switchboard corpus data (Godfrey et al., 1992), which is a corpus of a collection of natural and spontaneous telephone conversations, by Greenberg (1999, p. 167) indicated the following: Although only 22% of the Switchboard lexicon is composed of monosyllabic forms, approximately 80% of the corpus tokens are just one syllable in length [.


For example, when the measurement of wpm is used, it is not possible to say if the median (Mdn) utterance speed for "Okay" at 156.9 wpm was really spoken at half the intentional speed of "Got you" at 372.7 wpm.

by comparing the utterance duration of the subject and the duration of the TTS output, both utterances can be indicated with a more reasonable median (Mdn) speech rate value and say that "Got you" at 42.6% was spoken with a slightly faster speech rate than "Okay" at 33.29%, but not twice as fast as the wpm value would lead one to believe.


We would expect this kind of information in a generic summary about the topic.

each facet should also branch off and discuss the most important symptoms for affirming or excluding a diagnosis in one branch, as well as different procedures, their advantages and disadvantages, and evidence for their effectiveness in other treatment-specific branches.


The baseline system generated the shortest compression because all arcs of the WG have the same weights.

this system analyzes neither the grammaticality nor the most used n-grams in the clusters.


Systems capable of summarizing live streams of heterogeneous content can be directly beneficial to users and even assist journalists during their daily work.

this new task also comes with new challenges.


We took a snapshot of this page that provided us with 16,246 unique live blogs.

the BBC website has no such live blog archive.


(1) can compute the TF across all tweets.

the IDF is limited due to only one document.


It confirms the efficiency of the model in summarizing short texts (Inouye and Kalita, 2011).

a large margin between the ROUGE scores of the hybrid model and the upper bounds (Table 2) suggest that its performance can be improved.


The delay can be just a few hours, days or sometimes the news does not appear in the regional languages at all.

some content gets generated and consumed in regional languages alone.


Like PEAK, PyrEval implements pyramid construction and automated scoring.

to PEAK, it can also score target summaries against a manually annotated pyramid that has been produced with the DUCView annotation tool.


For instance, if someone tells about the last time they baked a cake, it is likely that they do not mention the fact that the cake was put into the oven, because it is obvious that this event took place.

a text understanding system that does not have access to script knowledge will probably not be able to draw this inference.


The low performance on the Equality cases is mainly due to the fact that it subsumes the difficult Diathesis and Phrasal Verb relations.

the results for Reverse Entailment are better than one would expect: This is mostly due to the fact that most Reverse Entailment cases (approx.


Target: influence (B2) Candidates: determine (B1), impress (A2), change A1In 2, where the target is the verb "influence" (B2), "determine" (B1) passes the grammatical reformation stage but fails at the definition stage because it does not cover the semantics of "influence" in the meaning of affecting the way someone thinks or behaves, though it might do so in another context where determination causes a change in someone's way of thinking.

the word "impress" (A2) passes this stage because it can be used to mean to affect the way someone believes.


Figure 1 shows the T_Precision curve and Figure 2 shows the T_Recall curve when varying the value of n. It turned out that LM and W2V have about 80% and 90% of T_Precision within the top five candidates respectively, which means a large portion of the targets that are assigned any candidates of CEFR-LS have at least one correct candidate in the top five words.

it can be said that W2V outperforms LM in that it finds a correct answer more quickly (60% T_Precision for the top-ranked word).


So far the development and operation of the Language Grid have been focused on supporting language service developers and end users.

application developers in international NPO/NGOs continue to struggle with available language services in creating multi-language systems.


O&M further note the Orientation is the starting state and the Resolution is the ending state.

we have not yet found a correspondence for Rising and Falling Actions in L&W's framework.


Strictly speaking, the first half of the next sentence "one night I was cycling home …" can be seen as part of Orientation.

for consistency we do not allow one sentence to be broken into multiple parts with different annotation labels.


The studies reviewed above exemplify a rich research tradition using statistical analysis of wine review corpora.

there are few studies that have applied natural language processing (NLP) techniques to such data.


A figure like William Shakespeare is widely acknowledged as important by literary critics because his works are meaningful and there is a great deal to be said about them.

if we remove Shakespeare from the network, we can then explore the extent to which the network is altered: is Shakespeare such a focal point that it breaks apart or do the connections occur with a high enough frequency that its shape is unaffected?


It is tempting to blame those differences on typological differences between the languages and to speculate about the features that make the German language so well-suited for authorship attribution.

as the sampling experiments show, the performance of the attribution methods varies considerably with corpus composition.


In his experiments, an average price was $0.15 per argument.

our experiments with AMT were unsuccessful due the German-fluency requirements on the workers.


The regionalism péguer 'to stick' has a fair recognition rate of about 0.6, but it has the highest standard deviation, meaning that its recognition rate varies widely across the area.

the question about the number 80 yielded exceptionally low recognition scores, due to the particular way the question was asked.


With both automatic methods, we reached the desired accuracy threshold with comparable area sizes and number of variables (about 20).

the variables selected by the SVM classifier intuitively corresponded better to the variation patterns observed in the original survey data.


Several works have attempted to link gradable adjectives with numerical quantities that co-occur in the context of the gradable adjective mention (Shivade et al., 2016;Narisawa et al., 2013).

this dependence on corpus resources to find evidence for gradability requires complex information extraction and suffers greatly from sparsity, especially when attempting to ground adjectives in a new domain.


This confirms that, indeed, language intuitions are more robust for high-frequency adjectives.

this effect is only seen in the full model when standard deviation is known.


While the specifics of the rules are language dependent, there is potential to enable linguists to describe these rules to the model in order to improve transcription in the language documentation setting.

this relies on identifying tone groups since the tonal rules do not hold across the dividing lines between tone groups.


The quantitative results reported up to this point are based on training, validation and test sets randomly selected at the utterance level.

this means the training set gets a fair representation of utterances from all the narratives present in the test set.


The small size of the data set would encourage novel approaches for a MT model, as there is not enough data to use many machine learning techniques.

as no system yet exists, a MT system would assist in generating new texts in Choctaw from English.


Text, speech, and video include different degrees of personal information and thus identification of the individuals who produced the data can vary (Jokinen 2011).

given present-day techniques of data processing, individual characteristics may be retrieved easily.


Several efforts have extended them to cover other dialects (Jarrar et al., 2014;Zribi et al., 2014;Saadane and Habash, 2015;Turki et al., 2016;Khalifa et al., 2016).

they focused on specific dialects and often made ad hoc decisions.


the assumption of one-to-one mapping is too strong to induce the many-to-many translation pairs needed to off-set resource paucity because few such pairs can be found.


These facts mean that their corpus-derived features are based on a larger number of occurrences in Gigaword and may have less diversity in their contexts (and hence features).

the operational and relative part nouns had very low recall, despite acounting for an intermediate number of examples (by number of unique token types).


Previous approaches to cognate transliteration (Mulloni, 2007;Beinborn et al., 2013) suffer from the drawback that they require an existing list of cognates, which is infeasible for low-resource languages.

we automatically generate cognate tables by clustering words from existing lexical resources using a combination of similarity measures.


php?products_id=1184, ELRA-W0063 cover several specialized topics related to the overall domain.

when working with topic specific corpora, some general domain terminology might not be perceived as such since the corpus does not offer broad view of the subject.


As with the previous measure, idf scores were also mapped on a scale of 0 to 100.

in the case of idf, we reverse the score so that the most "interesting" GEL candidates for our study receive a higher idf.


Figure 4 indicates that the specificity scores are useful to identify terminologically interesting lexical items.

for our current goal, which is to identify GEL entries, the usefulness of this measure is mitigated by the fact that valid candidates are scattered throughout the score range.


Cucerzan (2007b) and Han and Zhao (2009) described other algorithms for NERL.

to most of these previous works, multilingual support is at the core of HED-WIG.


(2013) propose universal schemas, which are the mapping between NL surface forms to the KB predicates, by using matrix factorization.

all of them suffer from low coverage on relational phrases.


Let an NL dataset be a 3-tuple ( , , ), where is a set of NL patterns, is a set of entities, and is a set of NL triples.

for example, (Garnett, was born in, Mauldin) DBpedia (Auer et al., 2007) serves as our target KB.


These types of approach also often combine dictionaries with manually defined heuristic rules (Gerner et al., 2010;Kang et al., 2013).

rules are timeconsuming to implement and highly dependent on the task and domain.


Therefore, the corpus primarily consists of dialogues.

these are not real dialogues that have been recorded and transcribed 2 http://gcp.nict.go.jp/about/index.html but pseudo-dialogues written by scenario writers imagining possible situations.


First, the BLEU scores are significantly different for each language, ranging from 22.05 to 52.87 for translation from Japanese and from 23.39 to 58.13 for translation to Japanese.

the score tended to increase with an increasing number of training sentences.


We can directly construct translators between all possible language pairs from multilingual parallel corpora (i.e., direct translation).

if we do not have such parallel corpora, we use pivot translation, which involves translating source sentences into target sentences via a resourcerich language known as a pivot (Utiyama and Isahara, 2007;Cohn and Lapata, 2007).


Both the pivot and zero-shot translation generally assume that bilingual corpora covering the source language and those that cover the target language are obtained from the different texts.

comparative analysis is difficult to perform under this setting because the vocabulary differs and we cannot construct a direct translator.


With the pivot translations, the BLEU scores for most language pairs were worse than those for direct translation.

the score for the Ko → Zh pair improved; thus, we can conclude that the pivot translation can achieve quality close to that of direct translation.


"The park" in En2-2 are extra words of Ja-2.

the speaker also spoke "I've never heard Tetsugakudo Park."


Human translators tend to translate literally between languages of the same family, such as English and French.

with language pairs for which it is difficult to make literal translations, such as English and Japanese, professional translators elaborately generate context-dependent translations to make the translations natural and the meaning of the dialogue identical.


There have also been some attempts in developing corpora annotated for negation in other languages as demonstrated by CNESP (Chinese Negation and Speculation corpus) (Zou et al., 2015), which closely follows the annotation style of the BIOSCOPE corpus.

tailoring the annotation style to a specific domain leads these corpora to differ in what was annotated and how.


To our knowledge, there has not been any previous work on projecting negation across languages.

previous studies have experimented with projecting semantic annotations via word-alignment information extracted from large parallel corpora.


(= It is not the case that money is everything).

let us consider the following example annotated according to the original guidelines.


If the negated event is only in the matrix clause, subordinates are usually excluded from the scope of negation.

chinese allows for it-cleft constructions like the one in (18), where only the subordinate clause, which appears before the event of the main clause, is in the scope of negation.


These false negatives are caused by the fact that Chinese translates positive terms in English as negation (same as the cases in (32)∼(34)) but in some cases are just due to English words aligning to a null token.

in 16% of the cases we observed that the event from English is projected onto a completely different span of the sentence.


Hindi is the fourth-most spoken language in the world, and third-most spoken language along with Urdu (both are registers of the Hindustani language).

english is spoken by just around 125 million people in India, of which a very small fraction are native speakers.


Hence, there is immense potential for English-Hindi machine translation.

the parallel corpora available in the public domain is quite limited.


For English, we used true-cased representation for our experiments.

the parallel corpus being distributed is available in the original case.


The more of these data are available, the better the quality of the SMT system.

for some language pairs such as Persian-English, parallel sources of this kind are scarce.


Parallel corpora are an important part of a statistical machine translation system.

there is a lack of such data available for everyone.


First, using character n-grams of size 5, instead of using the default range of 3-6, does not significantly decrease the accuracy (except for Czech).

using a smaller number of character n-grams leads to faster training, especially when using the CBOW model.


The extractive summarization methods are typically unsupervised, for example Luhn (Luhn, 1958), Latent Se-mantic Analysis (Steinberger and Ježek, 2004), LexRank (Erkan and Radev, 2004), TextRank (Mihalcea and Tarau, 2004), SumBasic (Vanderwende et al., 2007) or KL-Sum (Haghighi and Vanderwende, 2009).

very good results in extractive summarization were achieved recently with recurrent neural networks Nallapati et al., 2016b;Nallapati et al., 2017).


Initially, five Czech news websites were selected to create the dataset: novinky.cz, lidovky.cz, denik.cz, idnes.cz, and ihned.cz.

during the cleanup of the data, we decided to drop ihned.cz from the dataset, because too many of its pages turned out to be just abridged versions of the actual articles with links to paid content.


This suggests that their trend for these features may not have followed the same pattern over time.

some of the authors composed work some time before or after James and Twain and extrapolation may have caused a drop in prediction accuracy.


The evaluated systems, however, failed to produce any entry points for those queries.

we are not aware of previous attempts at labeling cross-document coreference using the event hopper framework, which focuses on annotators' intuitions about event reference and allows event argument mismatches as well as event mention realis mismatches.


It is fairly easy to conclude that d4 follows from d1 and d2.

considering only d3 as the source, although related but d4 has entirely diverse information.


Several approaches have been proposed in the literature and the current best practice is to evaluate them on a subset of the Reuters Corpus Volume 2.

this subset covers only few languages (English, German, French and Spanish) and almost all published works focus on the the transfer between English and German.


Based on the objective being sought, researchers focused their attention on classifying questions based on their subject (Conner, 1927), the educational objective (Bloom, 1956), the difficulty level (İnce, 2008) or the question goal (Lehnert, 1977).

we found that this domain lacks datasets and taxonomies that aim to analyze questions with respect to expected answer types.


Although multiple applications can benefit from analyzing questions based on this criterion, the majority of datasets and taxonomies were designed for question answering systems.

this type of classification can have multiple potential applications in educational systems as well, from facilitating student assessment to identifying the students' knowledge gaps in order to initiate classrooms discussions.


More specifically, 24% of disagreements were for the Con-textSensitive class, followed by VeryShortAnswer and Oth-erConstructedResponse with 18% each.

the annotators agreed in all cases when labeling Drawing  and Ordered questions.


This can be explained by the fact that these classes have clearer patterns in data and can be easily separated from the other class types.

identifying if a question elicits context sensitive information or a short versus a longer response appears to be more subjective, based on each annotator's interpretation.


This is an important observation regarding our data, since it was shown that involving deep questioning during tutoring can improve knowledge learning (Chi et al., 1994).

the least frequent question types in the data collected during tutoring are SelectN, Drawing and Equation.


Although Equation and Solution have less training examples, these classes possess clearer patterns in the data.

the worst performing class is Clarification, for which the simple classifier (strawman) was not able to capture patterns.


Like CRFs, RNN based model also rely on annotated corpus heavily.

we found that the RNN based model can identify more loanwords (more than person names) than CRFs based model, a possible reason is that the RNN encoder-decoder framework can learn features automatically and use its internal memory to process arbitrary sequences of inputs.


Distant supervision has been widely used in the task of relation extraction (RE).

when we carefully examine the experimental settings of previous work, we find two issues: (i) The compared models were trained on different training datasets.


This behavior can be observed for several languages, for English or German for example.

most natural language processing systems, parsers for example, rely on linguistic information only without taking further knowledge into account.


However, the terms juice and puree should not be weighted heavier than apple, since a match to one of the three food products containing apple is already a much better fit than a match to for example orange juice.

the term frequency is needed since some ingredient descriptions contain the same stemmed term multiple times and thus we assume that it is indeed more important than others.


An average recipe in the hobby collection consists of 12.7 ingredients and an average recipe in the catering collection has 20.5 ingredients.

not only the number of ingredients is different but also the ingredients themselves.


Originally, in analyzing puns, as with many other linguistic phenomena, it is necessary to analyze them reflecting the frequency of occurrence in the real world and conditions for occurrence (such as topics with high possibility of occurrence of conversation including puns).

with regard to the use of puns in everyday conversation, actual corpus and its statistical analysis result are not available so far and it is still difficult to find concrete and practical examples.


Hence, it can be said that the performance with respect to the unit requirement of the computational resource becomes maximum in the case of the linear kernel.

there still remains a strong need for a faster learning algorithm as accurate as an SVM with RBF Kernel.


The missing diacritics are not a major challenge to literate native adults.

their absence is the main source of ambiguity in Arabic NLP.


• Eastern zone (Mashriq): with dialects from Egypt, Syria and the others middle-east countries like Iraq, Golf states, Yemen, Oman, Jordan, etc.

this classification was refined, giving a new typology which was accepted by many researchers, such as (Versteegh and Versteegh, 2001;Habash, 2010).


• Inaccessible Tweets: We used Twitter widget to display the tweets on the task interface using the tweet ID.

when tweets are deleted or the author make his profile private, the annotators are no longer able to label them.


The first is a raw latitude longitude geo-tag which can give precise indication to region.

this is an optional feature most users do not use: only about 2% of tweets are geo-tagged for location (Huck et al., 2012).


Furthermore, a potential confound arises from the fact that a particular author's features may be easily identified across testing and training sets (Rangel et al., 2017).

the data has been completely anonymized and therefore further analysis regarding the influence of authorship is unavailable.


In the systematic reviews, the M are those articles that were excluded after reading the full text, and so are in reality negative examples.

our results suggest that these can still be used as positive examples for training.


Since the validation and test datasets were constructed by randomly choosing examples, the test performance on the model is conditionally independent on the validation performance given the model parameters.

the test performance is expected to be slightly worse in comparison to the validation performance since the model was chosen to achieve the best validation performance regardless the test evaluation.


The use of loss instead of other metrics such as accuracy or F-measure is supported by the fact that the loss itself models the actual behavior of the model.

in a classification problem, the accuracy of the model does not express its behavior in a sufficient detail.


On the contrary, the employment of dropout provides only approximately 10% accuracy gain.

with the previous experiment, the employment of the adversarial examples damaged the model performance in the case of bAbI #1.


(2015)), which corresponds to the most severe cases of trolling (Bishop, 2013).

we believe that it is important not only to identify trolling attempts, but also comments that could have a negative psychological impact on their recipients.


As an example, consider the situation where a commenter posts a comment with the goal of amusing others.

it is conceivable that not everybody would be aware of these playful intentions, and these people may disagree or dislike the mocking comments and take them as inappropriate, prompting a negative reaction or psychological impact on themselves.


Science changes continually: While certain research topics may be in a state of stagnation or decline, other research fronts move forward rapidly.

even "dormant" (Menard, 1971) science can regain importance if new data is produced or methods are developed to tackle unresolved research problems.


Such units are, indeed, characteristic of academic language and should not be ignored.

with the heavy skew observed, the ACL2 data set seems hardly usable for automatic prediction.


We have used ten-fold cross-validation during development to determine which classifier setup was more robust.

the classifier we used to actually tag the corpus is trained on the full dataset.


It is not straightforward to compare our classification result to the outcome of other annotation efforts, not only because there are only few such efforts, but also because annotation evaluation scenarios vary considerably.

qasemiZadeh and Schumann (2016) provide a detailed analysis of their manual annotation of the same data set.


For the 2000-2006 period, many of the lexical units highlighted by the rank shifts analysis in Technologies are related to recent work on the use of machine learning in computational linguistics, e. g. terms such as "classifier" or "feature".

ontologies have also gained importance and the 2000-2006 slice of Technologies includes novel lexical units such as "ontology learning", "ontology acquisition", or "ontology induction".


Linear CRF was used in (Schneider, 2006) in order to extract seven attributes about conferences from CFPs with the use of layout features.

in this approach only plain text of CFPs was used and layout features were based on lines of text, indicating, e.g., first token in line or first line in the text.


They used rule-based methods to extract information about conferences from conference services, like Wi-kiCFP, and combined them in one system in order to facilitate the process of finding conferences that are of interest for a user.

to aforementioned works (Xin et al., 2008) extracted information about conferences from web pages with Constrained Hierarchical Conditional Random Fields.


In contrast to aforementioned works (Xin et al., 2008) extracted information about conferences from web pages with Constrained Hierarchical Conditional Random Fields.

the set of homepages used in experiments has not been published.


The most effective compression is achieved by only storing differences between subsequent page revisions.

other compression techniques like BZip2, LZMA2 or none at all are also available and may be used when disc space is less an issue.


These experiments were conducted using Neo4J as backend.

wikiDragon is by design not limited to a specific backend but can be adapted to other database systems by implementing specific interfaces.


Regardless of the counseling method, counselors follow general principles, such as supporting autonomy, expressing empathy, centering on the patient and engaging patients using specific skills such as reflective listening (Charles et al., 1997;Harting et al., 2004).

using a more directing style -characterized by counselors providing instruction and advice, and patients obeying, adhering and complying (Miller and Rollnick, 2013) is usually avoided.


From the table we can see that providing a full conversation, both acoustic and textual representations are indicative of the speaker relationships.

the text based representation seems to have more information for this task, yielding 60.7% accuracy when using the Naive Bayes algorithm.


Not surprisingly, the most common types of semantic relationship that the appositive constructions reveal are those of role and ident.

personal relations such as friendship or fellowship are almost never made explicit   The PALAVRAS system recognized 797 cases of appositives and UDPipe 954 cases.


Companies use social data to gather insights on customer satisfaction, but can also relate this data to forecast product or services revenues (Rui and Whinston, 2011) or measure and optimize their marketing.

there are several levers that make social media such popular.


On one hand this massive social activity can transform a local cultural event into an international buzz feed.

major festivals that do not follow the social mainstream could fail in attracting and renewing the public.


One of the major advantages of neural machine translation (NMT) is that unlike statistical machine translation (SMT), which was the previous industry standard (and is still actively used in commercial applications), NMT is trained and used jointly as a single end-to-end system without the need to optimize multiple independent models and relations between the models.

training NMT systems for individual language pairs has shown to take significantly more time (e.g., two to three weeks or up to a week with newer platforms, such as Marian (Junczys-Dowmunt et al., 2016) or Google's Tensor2Tensor toolkit 1 ) than training of SMT systems (e.g., less than a day or up to several days for large systems).


The results show that the GRU multi-way model outperforms the one-way models for all language pairs on all datasets.

the convolutional and transformer models increase quality only for the low-resource language pairs.


For the low-resource language pairs, the best results were achieved by the multiway model.

for the high-resource language pairs, the best results were achieved by the respective one-way models.


A practical solution to this limitation is to make use of comparable corpora (Rocheteau and Daille, 2011;Xu et al., 2015;Hakami and Bollegala, 2017) that are available in large quantities.

term extraction along this line is often limited to noun phrases (< 5 words) from monolingual comparable corpora.